# recall.cmatrix.md

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| recall.cmatrix | R Documentation |

### Recall

#### Description

A generic S3 function to compute the _recall_ score for a classification\
model. This function dispatches to S3 methods in `recall()` and performs\
no input validation. If you supply NA values or vectors of unequal\
length (e.g. `length(x) != length(y)`), the underlying `C++` code may\
trigger undefined behavior and crash your `R` session.

**Defensive measures**

Because `recall()` operates on raw pointers, pointer-level faults (e.g.\
from NA or mismatched length) occur before any `R`-level error handling.\
Wrapping calls in `try()` or `tryCatch()` will _not_ prevent `R`-session\
crashes.

To guard against this, wrap `recall()` in a "safe" validator that checks\
for NA values and matching length, for example:

{% code overflow="wrap" lineNumbers="true" %}
```r
safe_recall <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  recall(x, y, ...)
}
```
{% endcode %}

Apply the same pattern to any custom metric functions to ensure input\
sanity before calling the underlying `C++` code.

**Efficient multi-metric evaluation**

For multiple performance evaluations of a classification model, first\
compute the confusion matrix once via `cmatrix()`. All other performance\
metrics can then be derived from this one object via S3 dispatching:

{% code overflow="wrap" lineNumbers="true" %}
```r
## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate recall
## via S3 dispatching
recall(confusion_matrix)

## additional performance metrics
## below
```
{% endcode %}

The `recall.factor()` method calls `cmatrix()` internally, so explicitly\
invoking `recall.cmatrix()` yourself avoids duplicate computation,\
yielding significant speed and memory effciency gains when you need\
multiple evaluation metrics.

#### Usage

```r
## S3 method for class 'cmatrix'
recall(x, estimator = 0L, na.rm = TRUE, ...)
```

#### Arguments

| `x`         | A confusion matrix created `cmatrix()`.                                                                                                                                                                                                                                                                                                                                                                                                                                          |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `estimator` | <p>An &#x3C;integer>-value of length <code>1</code><br>(default: <code>0</code>).</p><ul><li>0 - a named &#x3C;double>-vector of length k<br>(class-wise)</li><li>1 - a &#x3C;double> value (Micro averaged metric)</li><li>2 - a &#x3C;double> value (Macro averaged metric)</li></ul>                                                                                                                                                                                          |
| `na.rm`     | <p>A &#x3C;logical> value of length <code>1</code><br>(default: TRUE). If TRUE, NA values are removed from the computation.<br>This argument is only relevant when <code>micro != NULL</code>. When<code>na.rm = TRUE</code>, the computation corresponds to<code>sum(c(1, 2, NA), na.rm = TRUE) / length(na.omit(c(1, 2, NA)))</code>.<br>When <code>na.rm = FALSE</code>, the computation corresponds to<code>sum(c(1, 2, NA), na.rm = TRUE) / length(c(1, 2, NA))</code>.</p> |
| `...`       | Arguments passed into other methods.                                                                                                                                                                                                                                                                                                                                                                                                                                             |

#### Value

If `estimator` is given as

* 0 - a named \<double> vector of length k
* 1 - a \<double> value (Micro averaged metric)
* 2 - a \<double> value (Macro averaged metric)

#### Other names

The Recall has other names depending on research field:

* Sensitivity, `sensitivity()`
* True Positive Rate, `tpr()`

#### References

James, Gareth, et al. An introduction to statistical learning. Vol. 112.\
No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining,\
inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python."\
the Journal of machine Learning research 12 (2011): 2825-2830.

#### Examples

```r
## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
x = sample(x = classes, size = 1e3, replace = TRUE),
levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
x = sample(x = classes, size = 1e3, replace = TRUE),
levels = c("Kebab", "Falafel")
)


## Construct confusion
## matrix
confusion_matrix <- SLmetrics::cmatrix(
actual    = actual_classes,
predicted = predicted_classes
)

## Evaluate performance
SLmetrics::recall(confusion_matrix)

```

```

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